Generative AI

SALT LAKE CITY — As CX leaders across industries descended on Salt Lake City for Qualtrics’ X4 conference in March, they couldn’t help but talk about AI. Sure, other topics were the main focus, but many sessions came back to how AI is changing how companies operate.

Qualtrics XM Institute and McKinsey unveiled data on how many executives remain wary of AI investments even though they recognize their potential. Sam’s Club and Chime discussed how they are implementing AI without removing the people from customer experience.

These are just two examples of the enormous amount of thought companies are putting into their tech rollouts. Here is a look at four more, from using generative AI to send data crunching capabilities to the moon to using the technology to help humans focus on their real jobs. 

Verizon goes from data wagon to data rocketship

Verizon likens its pre-AI data capabilities to a little red wagon, according to Deborah Campbell, VP of customer marketplace insights. It was a useful tool they could pull along, but ultimately a very manual process.

The wireless provider has greased the wheels of that wagon with AI. Now, the next step is to turn it into a rocketship, according to Campbell. The upgraded vehicle will enable speed and complexity while smashing down silos.

“We’ve looked at our entire CX ecosystem, and we are going to now move completely away from channel-based measurement to journey-based measurement, where we will be able to look at the journey by channel, by customer, segment, by activity, by initiative,” Campbell said.

The goal is to ingest call transcripts, chat transcripts and social media conversations and combine them into a single source of insights, according to Campbell. 

Qualtrics foresees personalization’s democratization

While AI agents are poised to reimagine customer experience, no one wants to be treated like a cog, according to Manisha Powar, chief product officer of Qualtrics XM for customer experience. 

Automation shouldn’t make customers feel “optimized” with interchangeable experiences, Powar said. People want personalization, and AI can help smaller companies close the gap with their larger counterparts.

“All of that was really hard to do,” Powar said. “You had to be a bazillionaire company, a visionary leader, who baked the right QA experience. Only the largest could afford that. Creating a personalized experience isn’t a pipe dream anymore.”

Qualtrics isn’t the only vendor working on democratization, but Powar wants to be on the forefront of creating those personalized experiences. The goal is to have every interaction make customers feel like they matter. 

Indeed reduces workers’ cognitive workload

Indeed only gets one shot to make its site experience work, according to Adam Hagerman, director of UX research. If a user feels frustrated, they’re likely to move on to a competitor.

This makes optimizing the job search experience essential, but it can be difficult to create a universally positive experience for a site that operates globally. AI is helping Indeed cut through the noise of its customer listening data so employees can pinpoint the parts that matter.

“What it offers us is an opportunity to remove what I’m hearing people call ‘cognitive overhead,’” Hagerman said. “It’s not so much that the AI is doing all the work for you. It’s doing the stuff that prevents you from doing the real job.”

AI is helping the company “deal with all this information coming out of us like a fire hose” and condense it into a single version of the truth that lets all its teams unite around common goals.

ServiceNow puts agentic AI to work for people

AI could have a $20 trillion impact on the global economy, according to Bill McDermott, CEO of ServiceNow. He expects every $1 wisely invested in agentic AI to generate $5 in return.

The greatest potential the technology offers is as a complement, not necessarily a replacement, for people, according to McDermott. ServiceNow now solves 80% of cases that used to route through live agents with agentic AI, but the change didn’t lead to cuts.

“Agentic AI didn’t replace a single person, because I’m a growth company,” McDermott said. “So companies are going to be able to grow again. They’re going to be able to reinvent business models if they do this as well.”

However, McDermott doesn’t expect every company to have the same results. Businesses need to manage their operating expenditures to realize the full value of AI, removing unnecessary costs wherever they appear, and that could include cutting jobs. 

The trick is that the changes can’t come at the expense of the experience.

It’s becoming the norm for consumers to use generative AI tools and social media when deciding what to buy, a survey by Capgemini of 12,000 global shoppers found. 

Nearly 3 in 5 consumers have replaced traditional search engines with generative AI and over two-thirds of shoppers want the tech to aggregate search results from online search engines, social media platforms and retailers’ websites.

Roughly two-thirds of millennials and Gen Z, closely followed by Gen X, have replaced traditional search engines with generative AI tools such as ChatGPT for product recommendations, the survey found. Just 44% of baby boomers use generative AI when shopping.

Consumers are growing reliant on social media as a primary source across the entire shopping experience, from discovering products to making purchase decisions and engaging with retailers. 

Nearly one-third of shoppers used social media to buy products and identify new brands last year, up from one-quarter in 2023. That number is even higher for Gen Z shoppers, where more than two-thirds have discovered a new product or brand on social media.

“Social commerce is the retailer’s route to Gen Z,” Capgemini found. Over half of Gen Z have bought products through a social media platform.

Beyond recommendations, consumers are also looking to social media for answers to questions about products, for reviews and to reach the retailers they purchase from. Over half of consumers occasionally use social media to search for product information and reviews. Two in five shoppers use social media for customer service. 

Other reports have shown that Gen Z looks to social media the most for making purchase decisions. Over half of Gen Z have made an online purchase while browsing social media, a survey by Walmart and Morning Consult found. 

Research from the ICSC found the vast majority, 85%, of Gen Z says social media impacts their purchase decisions, particularly TikTok and Instagram as the top platforms.

Generative AI has made similar strides, but across all generations of consumers. Nearly 3 in 5 respondents to an Adobe survey last year said generative AI has already improved their online shopping experience. Two-thirds of survey respondents want brands to analyze previous purchases and customer information to personalize the shopping experience.

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By Talkdesk

Customers expect fast, personalized service across multiple channels, and businesses are relying on chatbots and voice virtual agents to meet expectations.  While both tools aim to enhance efficiency and streamline communication, they serve different purposes.

These bots handle simple, scripted tasks and quickly respond to routine inquiries. However, they fall short when addressing complex or emotionally nuanced issues that demand a deeper understanding.

There is a better way! AI-powered virtual agents leverage agentic AI to think, adapt and engage in more sophisticated conversations, offering empathy and context awareness that typical pre-scripted chatbots can’t match. Their ability to learn and improve over time enables businesses to provide accurate and sophisticated support.

Going from static dialogue flows to smarter, adaptive responses.

Chatbots are built to follow scripts. They’re programmed to answer questions in a predetermined way, often relying on intent recognition. If a customer asks a question outside of the bot’s programmed set of intentions, it may get stuck, offer irrelevant answers or loop back to basic, unhelpful answers like, “I’m sorry, I didn’t understand that.” These limitations can frustrate customers, especially when they have more complex issues.

AI agents have moved beyond these static scripts and use large language models (LLMs) to understand human language better, enabling them to handle more complex interactions. This allows AI agents to understand what the customer is asking and the context and underlying purpose behind the query. For example, if a customer asks, “Can I change my flight?” a chatbot answers with a generic answer that is pre-defined. At the same time, an AI agent can dig deeper into the customer’s intent and understand that this might involve checking current bookings, rebooking fees or even offering alternative solutions if a direct change isn’t possible.

AI agents continuously improve their understanding of human language, handle more complex queries and provide meaningful responses. This ability to interpret intent allows them to engage in more natural, human-like conversations, improving customer satisfaction by up to 120%.

AI agents create frictionless customer experiences.

While chatbots can’t summarize interactions or provide detailed handovers, AI agents can generate comprehensive automatic conversation summaries, including interaction history and relevant details, whether for finished interactions or those transferred to human agents.

If the AI agent handles the issue autonomously, it completes the process—generating interaction summaries, disposition codes and others—just like a human agent would. If it needs assistance, the human agent has access to all the information gathered by the AI agent to seamlessly continue the conversation without the customer needing to repeat themselves. All information is stored and accessible by other agents, virtual or human, for future reference.

AI agents don’t require scripting or coding.

One of the main barriers to implementing and maintaining chatbots is the required technical expertise. Traditional chatbots need resource-intensive model training, costly infrastructure, the expertise of data scientists and ongoing maintenance to keep the models accurate and relevant. IT resources are limited, and the ones available come at a high cost, making it challenging to manage without stretching the budget. Additionally,  the dependency on specialized resources limits AI’s potential to scale and adapt in real time.

AI agents don’t require any coding knowledge to set up or maintain. Talkdesk Autopilot, powered by agentic AI, uses a visual, no-code tool enabling non-technical users to configure the AI agent using plain English prompts (or any other human language) and defined goals. This breakthrough makes AI-driven automation more accessible and scalable across industries.

AI agents integrate with external tools.

In addition to being simple to set up, AI agents require access to external tools to interact effectively with the world. These tools, often API-driven, enable AI agents to retrieve and modify information within external systems. Consider a scenario where a customer wants to change a flight. The AI agent identifies key details—such as alternative dates, seat classes or destinations—and then guides the conversation autonomously to gather the necessary details before using the right tool to complete the request.

AI agents speak industry-specific language.

Chatbots can answer questions about store hours, shipping policies or account management but can’t engage in more specialized conversations that often require industry-specific jargon.

AI agents can understand and respond in industry-specific language, enabling them to provide accurate and relevant support in specialized fields. For example, an AI agent in the healthcare industry can recognize medical terminology, understand insurance jargon and comply with regulations like HIPAA. In finance, AI agents can assist with more complex tasks like credit inquiries, loan applications or fraud detection while adhering to industry regulations like GDPR or PCI-DSS. Speaking and understanding the language of the industry allows AI agents to offer a more sophisticated and relevant customer service experience.

Guardrails keep AI agents on track, no hallucinations.

One of the key challenges of AI-powered systems, especially those that rely on LLMs, is the risk of hallucinations and AI going rogue. AI can generate incorrect or misleading information that seems plausible but is wrong or biased responses—in addition to the ethical risks associated with the hallucinations—which can harm the customer experience and damage the company’s reputation.

AI agents blend transactions with knowledge.

Customer interactions often involve complex, real-time decision-making. Questions like, “Will I need to pay a fee for this change?” frequently arise during conversations. Traditional, scripted chatbots struggled to handle off-script inquiries, often disrupting the user experience.

AI agents, on the other hand, seamlessly blend transactional and contextual knowledge. By accessing a repository of trusted knowledge articles, they can provide accurate answers without derailing the ongoing transaction, ensuring a smooth, uninterrupted customer journey.

How it works: The plan-and-execute paradigm.

The plan-and-execute paradigm is an AI approach that enhances how agents perform tasks by structuring their actions into two key phases: planning and execution.

1. Planning phase. The AI agent first outlines all the steps required to complete a task before taking action. This allows for a more structured and efficient workflow rather than reacting step-by-step without a clear strategy.
2. Execution phase. Once the plan is created, the AI agent performs the steps with minimal interruptions, leading to faster task completion, reduced computational costs and better overall performance.

This paradigm is particularly beneficial because it allows AI agents to handle complex, multi-step workflows more effectively, adapt dynamically to changes and minimize reliance on large AI models for every decision. It marks a shift from rigid, scripted AI behavior toward more intelligent, autonomous problem-solving.

Real-world applications of AI agents.

The potential applications of these AI-driven advancements are vast. They improve not only operational efficiency but also provide new opportunities for innovation. Some key use cases include:

• Complex automation tasks. AI agents automate complex tasks, such as in-depth data gathering and synthesizing information into actionable insights. For example, AI can autonomously collect data from multiple sources and provide meaningful recommendations, greatly reducing the time and resources required to perform these tasks.
• Multi-step problem-solving. AI can handle intricate workflows involving decision-making at each stage with minimal human intervention. For example, in retail, AI can analyze buying patterns and customer behavior to predict trends, adjust stock levels and set dynamic pricing to save time and increase profitability and scalability.
• Dynamic customer support. AI agents provide contextual, adaptive customer service. Unlike traditional chatbots, they respond intelligently to evolving user needs, continuously learning to offer personalized solutions.

From bots to AI agents: Embracing smarter solutions.

Chatbots served their purpose well in the early days of automation, offering quick, simple responses to routine questions. But as customers demand more personalized, proactive and accurate support, AI agents have emerged as the smarter solution.

They go beyond static scripts, offer a deeper understanding of customer intent and more context-aware conversations, ensure smooth transitions and efficient resolutions—even for industry-specific needs—and stay on track with guardrails. These capabilities enable AI agents to handle even the most complex customer service scenarios without sacrificing accuracy or reliability. AI agents are the next step in customer service evolution, offering a way to meet rising customer expectations, improve customer experiences and reduce costs.

We are just starting to tap into AI agents’ full potential. The future lies in architectures capable of planning, executing, learning and adapting—similar to an intelligent assistant that fully grasps context and complexity. As these systems evolve, they will redefine automation, efficiency and user experience across industries.

Consumers have quickly adapted to generative AI tools when shopping online, according to Capgemini Research Institute’s 2025 consumer trends report.

Seven in 10 respondents are keen to have it integrated into their purchasing experiences and nearly half — 46% — are enthusiastic about the impact of generative AI on their online shopping, the global survey of 12,000 adult consumers found.

As generative AI changes consumer shopping behavior, brands will need to quickly adapt to facilitate conversational search and create tools that quickly and seamlessly guide customers to products and services, experts said. With first-mover advantage going to forward-looking brands, now is the time to get started.

With customers favoring the convenience and personalization of generative AI tools, brands have an opportunity to improve customer interactions by adapting and optimizing experiences based on behavior patterns, according to Gabriel Bridger, global head of experience and strategy at Rightpoint.

“The best part is that [generative AI tools] can be personalized and continuously iterated without disrupting the entire experience,” Bridger said.

The major benefits of AI-powered shopping tools are saving time, easier navigation and better product recommendations, Bloomreach has found.

Eyeing the opportunity to improve product search across its mammoth catalogue, Home Depot recently introduced its Magic Apron tool. It’s an AI-powered digital store assistant that streamlines and simplifies the product search while continuing to offer standard online navigation.

Digital design experts such as Bridger believe tools like Magic Apron are an example of using the technology to address genuine pain point that can translate into better customer experience.

“It offers another smart way to navigate the dense product details and specifications in a simple chat-based interface,” Bridger said.

Where should brands start?

To get started, brands can focus on adding conversational tools to existing structured navigation to improve the online experience and respond to changing customer behavior.

“Brands should start by layering AI-powered enhancements into existing systems, rather than replacing them outright,” Bridger said. “It sets the stage for brands to test and learn their way into the future.”

By layering the technology, businesses can continue to cater to consumers who favor menus and structured navigation while offering interactive tools as well.

Bank of America’s virtual assistant Erica is a case in point. The bank combines structured menu options with AI-driven conversations to provide financial insights and transaction assistance.

To maximize engagement and customer benefit, these tools must align with the customer’s preferences and their expectations of the brand, Bridger said.

For example, L’Oréal’s AI assistant Beauty Genius blends traditional navigation with conversational AI. The virtual beauty assistant offers personalized product recommendations and beauty routine suggestions.

“It depends on who your customer is, what they need, and how the experience is designed,” he said.

The risk vs. reward of generative AI

Ignoring AI could translate into lower engagement and customer retention. Now is the time for brands to take their first steps by experimenting with generative AI, even though they may be unsure of the outcome, experts told CX Dive.

“There’s a lot of CapEx spend going into AI at the moment with not a lot of guarantees, so it’s very high risk,” said Tadhg McCarthy, founder and chief design officer at Elsewhen.

Despite this, he believes that the biggest risk is getting left behind.

“Without these experiments, they could miss this fundamental shift in how customers want to engage with their brand and having their systems prepared for that,” he said.

There’s usually significant work so that internal data systems are compatible with the technology, according to McCarthy. In particular, siloed data and lack of interconnected systems are the most common challenges. 

“The path forward is creating the digital piping and finding ways for AI agents to connect to your systems,” McCarthy said.

This restructuring work will position brands to embrace AI agents that can offer recommendations, track orders and make simple transactions on behalf of consumers.

Align metrics and tread carefully with data to deliver CX gains

To prove their worth, generative AI tools must drive measurable improvements in customer engagement. Customer adoption and engagement rates are the most obvious way to track their effectiveness, according to Bridger.

“Use CSAT, NPS and sentiment analysis to gauge user satisfaction and even how it ultimately affects brand affinity,” Bridger said.

However, Bridger warns customer expectations rise quickly and limitations in the technology can turn into disappointing experiences that show up in brand metrics.

“AI that doesn’t remember past interactions or misunderstands simple concepts can turn customers off and also reflect poorly on the brand,” he said. 

To build trust and encourage adoption, brands must also be careful about the amount of data they’re capturing and how and where it’s used. Too much personalization can feel invasive and lead to a lack of trust if not handled properly, according to McCarthy.

Education can help customers understand what personal information they’re handing over when using AI systems, but brands should only capture what’s necessary. Failing to tread responsibly, could backfire.

“It will come back to bite these companies in the end if they don’t figure out how to use customer data without abusing their position,” said McCarthy.

AI has been deployed for years in customer experience settings, but it was an expensive, time-consuming exercise to train the models and required ongoing maintenance to analyze sentiment and other signals, according to Jonathan Rosenberg, CTO and head of AI at Five9.

“Then generative AI came along and changed all of this,” Rosenberg said.

Many business leaders expect the technology to transform operations in the coming years, improving efficiency and productivity and reducing costs, according to a 2024 Deloitte Digital report. Customer service is one of the top use cases for generative AI, helping agents rapidly build rapport and elevate experiences and acting as a force multiplier.

“It’s possible to analyze every conversation in the contact center by feeding the content of the conversation to a generative AI model and asking it to assess sentiment, customer satisfaction, issue resolution and many other metrics,” Rosenberg said.

But it doesn’t stop there: The technology can then aggregate all this information, providing the business with a bird’s eye view of what’s happening in the contact center.

“We can now automatically detect new topics of conversation in the contact center and alert managers to these topics so they can proactively take action,” Rosenberg said.

Alaska Airlines has deployed Five9’s Intelligent CX Platform for automating calls routing based on channel volume, agent skill set, and availability. The airline was able to lower wait times, even during call spikes.

“Real-time call transcriptions let agents read what a guest is saying in real time, improving their comprehension and engagement,” Rosenberg said.

Aeroflow Health, a medical equipment and supply provider, combines multiple AI tools to streamline customer inquiries. 

This approach has increased self-service options, decreased average handle times and reduced call volume, while AI-generated summaries enable Aeroflow to test, monitor, and improve its models to optimize the output of its call summaries.

From chatbots to agent assistants

Human intervention remains essential, and generative AI can enhance the traditional 360-degree customer view, prioritizing key insights to guide agent interactions.

In addition to outward-facing tools such as chatbots, internal assistants can support customer service teams and show the potential of the force multiplier effect.

“Conversational AI, especially in live chat and messaging, has matured significantly, but with generative AI, it’s shifted inward, focusing on augmenting employees who interact with customers,” said Jason Valdina, senior director of go-to-market strategy for digital engagement channels at Verint.

When it comes to customer support, these tools excel at summarizing interactions and extracting useful data points, such as language and caller history.

“Summaries help agents quickly grasp the context, reducing average handling times by approximately 50 seconds per interaction,” Valdina said.

By combining tools, businesses can realize more significant results. For example, Volaris airlines, a major Latin American carrier, deployed knowledge automation and coaching tools, freeing up agent capacity and redirecting agents’ attention.

“Agents, with newfound bandwidth, could upsell services, such as discounted seat upgrades, and they were able to offset the entire cost of their contact center,” Valdina said.

Sentiment analysis is another area of customer support that can be improved by applying generative AI tools. A coaching tool that operates as a copilot alongside an agent’s workflow can intervene with prompts that provide guidance, from improving communication etiquette to ensuring accurate responses and gauge customer and employee sentiment in real time.

“One healthcare provider saw a 16% increase in net promoter score after implementing a scoring tool that monitored and intervened in conversations with declining sentiment,” Valdina said.

Other tools can create summaries of ongoing conversations when transferring interactions between agents and at the end of an interaction to make a record of the call, saving on average 30 seconds and 60 seconds, respectively, according to Valdina.

However, consumers are concerned about not being able to speak to a human. While generative AI tools can help save time, they are not a replacement for human touch.

“The emphasis is not on replacing human agents, but on enhancing their efficiency, enabling them to handle more tasks effectively,” Valdina said.

 

Walmart is accelerating its Adaptive Retail strategy, aiming to combine generative AI, augmented reality and personalization to create immersive experiences in Walmart and Sam’s Club stores, apps and websites, the company said in October.

The retailer has developed a series of LLMs called Wallaby and plans to use the technology to support customer-facing assistants and experiences. Walmart has trained Wallaby on decades of internal data, which lets it work with other LLMs to create responses that are highly tailored to the Walmart environment.

Walmart also created an AI-powered Content Decision Platform that tries to predict what customers want to see on its website to create unique homepages for every customer based on their interests. The updated website is expected to launch in the United States by the end of 2025.

Walmart is continuing to invest in AI and personalization technology aimed at creating experiences that help the retailer stand out from the competition.

The retailer has been rolling out generative AI tools throughout the year, with an emphasis on improving search and discovery. Walmart added enhanced search to its app in January, and began testing an AI shopping assistant with select customers in June.

“A standard search bar is no longer the fastest path to purchase, rather we must use technology to adapt to customers’ individual preferences and needs,” Suresh Kumar, global chief technology officer and chief development officer at Walmart, said in a prepared statement.

The average customer spends 6 hours searching and browsing online every week, Anshu Bhardwaj, SVP and COO at Walmart Global Technology and Walmart Commerce Technologies, told CX Dive at National Retail Federation’s conference in January 2024. The generative AI updates aim to cut this time by letting customers use natural language queries and receive more accurate responses in return.

Walmart isn’t the only one updating its search with AI. Google rolled out AI-powered search updates in October. They include improvements to the Google app’s Lens feature, which lets customers take photos of items and then search for similar products, and curated results pages for recipe and meal inspiration searches.

Walmart’s tech investments are aimed at enhancing in-store experiences as well. The company used generative AI to create or improve the quality of more than 850 million pieces of data across its product catalog. This information connects to store and warehouse associates’ mobile devices, helping them find products in the backroom or put together deliveries faster. 

Walmart credited associate-facing tools for a successful holiday season in 2023.

Walmart is expanding shopping experiences to new channels too. The company developed an augmented reality platform called Retina, which will let customers shop in virtual social environments, though Walmart has yet to provide specifics on the experience or a launch date.

In a bid to improve efficiency and quality in its customer support, streaming services company Brightcove became an early adopter of Amazon Q, a generative AI–powered assistant for its in-house “Expert Bot.”

When approached by Amazon, Brightcove executives immediately saw the potential to better handle technical queries from its diverse range of global clients that include the SXSW conference, Consumer Technology Association, Cancer Treatment Centers of America and the Academy Awards.

Brightcove support agents field thousands of queries, from simple instructions to reset a password to more complex help like embedding video in a WordPress site that can require significant time.

“In the past, answering this question would require a support agent to manually search through documentation and provide a detailed response,” said Scott Levine, chief product officer at Brightcove.

With the new tool, the company says it is saving time and improving its already highly regarded customer service.

“The starting point for us was ’Can we make it easier for us to answer every single one of those questions and make ourselves do better?’” Levine said.

Delivering value in the rush to adopt generative AI tools

The Brightcove example reflects a wider trend of organizations adopting generative AI across a range of business functions, showing they can deliver real business value, a 2024 McKinsey survey found.

To gain a genuine competitive advantage, however, businesses should focus on helping people do their current jobs better and lift productivity, according to the research firm.

If generative AI tools improve the way employees provide customer service and support, it can have a real impact on the business, according to some experts.

“How you service and support customers, whether your business offers a product or an interaction, is becoming more of a differentiator,” said Melissa Copeland, founder and principal of Blue Orbit Consulting, which specializes in customer experience design and technology adoption.

The challenge is applying the technology where it delivers a dividend, not just for automation or cost cutting.

“Organizations must find the opportunity to use technology to reduce friction for customers and reduce friction for agents,” Copeland said.

Copeland cites examples of businesses that have seen a minimum 25% to 35% return on investment in the form of decreased handling time or first call resolution when deploying generative AI customer support tools. The gains could expand beyond, too.

“Then there’s thinking about new metrics beyond the traditional ways. That’s what’s really exciting about the possibilities of these tools,” she said.

Build generative AI tools with guardrails to foster trust

To deliver on the promises of generative AI, organizations must ensure the accuracy of the model to build trust, according to McKinsey.

In Brightcove’s case, it took a cautious approach, setting well-defined guardrails around the use of customer information that require permissions, rigorously testing the system, and having in-house technical experts scrutinizing the output.

“It’s not one person looking at it; it’s 50 people looking at it,” Levine said.

The Amazon team worked closely with Brightcove staff, teaching them how to craft questions to get the most suitable answers and taking time to train the bot on its extensive library of documentation as the source for responses.

“Streaming is complex, and that creates a lot of information about how to do it the best way across thousands of customers,” Levine said.

The bot-generated answers also include citations that link to the Brightcove source information so if the answer doesn’t look right, human experts can verify the response and feed that back to the tool. It adds an important layer of trust and teaches the model to help improve its output as it processes more queries.

“We have a lot of very technical people here at Brightcove, and when we see answers that aren’t right, we call it on it,” he said.

The end result is that the bot provides customer and product support teams with responses to customer questions. Brightcove has found it’s not only reduced the response time but also improved the overall efficiency of the support team.

“It’s about giving our customers the information they need quickly and accurately, and continuously improving that process,” he said.

Avoiding the trap of one-off tools

To maximize the potential of generative AI, McKinsey recommends that organizations avoid one-off solutions and invest the time and resources into tools that will scale.

Copeland cautioned that organizations must think through the longevity of the platform they’ll be working with.

“Use as your base platform a known entity, such as a large outfit that already offers customer experience tools you’re using across the business,” Copeland said.

While organizations might want to experiment, they also need to avoid different internal groups building their own tools independently to ensure widest application of new tools.

“If you get one-off customizations, it’s difficult to pull together, and when things are done in bits and pieces without any kind of central coordination, it becomes a nightmare to centralize and scale,” Copeland said.

In the case of Brightcove, Levine sees ways to expand the company’s use of generative AI to address more complex tasks such as enhancing metadata management on video streams or automating translation services to convert a video into multiple languages.

“Our customers have footprints all around the world, and if we can enable them to translate content on the fly, that’s another use case for generative AI,” Levine said.

Above all, Brightcove’s use of Amazon Q is meant to not only improving operational efficiency but also add value to its service for customers.

“Everything we do starts with that mindset and driving business value,” he said.